Machine learning MLNG

Developing systems that learn through experience and by the use of data.

Guidance notes

Activities may include — but are not limited to:

  • evaluating trained models for their performance, robustness and bias
  • selecting and using metrics to examine outcomes
  • diagnosing and resolving issues before and after deployment
  • anticipating the organisational implications of machine learning models regarding ethics, bias, privacy, and data protection
  • establishing traceability for the outcomes produced by machine learning systems.

Machine learning: Level 2

Applies given machine learning techniques to data, under the guidance of technical leadership.

Analyses and reports findings and remediates simple issues using algorithms implemented in standard software frameworks and tools.

Machine learning: Level 3

Applies existing machine learning techniques to new problems and datasets.

Evaluates the outcomes and performance of machine learning systems.

Identifies issues and recommends improvements to machine learning systems and the data they use.

Machine learning: Level 4

Given a well-described problem and dataset, assesses whether machine learning is likely to provide an effective solution.

Implements algorithms developed by others. Advises on the effectiveness of specific techniques, based on project findings and wider research.

Contributes to the development, evaluation, monitoring and deployment of machine learning systems.

Understands and applies rules and guidelines specific to the industry, and anticipates risks and other implications of modelling.

Machine learning: Level 5

Designs, implements, tests and improves machine learning architectures and systems.

Selects techniques based on a breadth of knowledge of the strengths, weaknesses and expected performance of different approaches.

Establishes good practice in the development, evaluation, monitoring and deployment of machine learning systems.

Machine learning: Level 6

Leads the development of new approaches and organisational capabilities to design, train, and evaluate machine learning systems.

Sets standards and guidelines for the application and traceability of machine learning systems to business problems, and oversees their implementation.

Designs and oversees organisational policies on the creation, training and use of machine learning systems.